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Introduction

Stem cell therapy is an approach for treatment of heart diseases since the first intracoronary implantation of autologous bone marrow mononuclear cells (Strauer, Düsseldorf) and the first intramyocardial implantation of purified CD133+ bone marrow stem cells (Steinhoff, Rostock) in 2001 in patients with ischemic heart failure [6, 8]. Nevertheless, the standard criteria for evaluation of safety and efficacy of stem cell therapy were not elaborated. According to Good Clinical Practice an insurance of safety of participants is required during and after studies. Short (1-2 years) medical observation after procedure, which is usually planed in majority studies, could unlikely satisfy the requirements of follow-up vigilance analysis. Moreover, parameters for efficacy evaluation of stem cell treatment are broad and include functional parameters as well as data of radioisotope imaging technique, quality of life and ect. The common registry program embracing all stem cell centers could help to solve named problems. In particular, it would be possible to follow probable late complication and determine the most relevant outcome parameters after stem cell therapy in the heart. Moreover, this program might be used for a standardized pharmacovigilance reporting to regulatory authorities. In addition, common stem cell register could be helpful in evaluation of the most relevant outcome parameters in patients with heart disease. Unfortunately, it is not yet exist. Meanwhile, in the Regeneration and Translation Center for cardiac stem cell therapy in Rostock, Germany (RTC) the registry program following yearly lifelong condition of patients after stem cells procedure was established 14 years ago. We would like to present our experience with stem cell register in patients with ischemic cardiomyopathy and describe the revealed relevant parameters for evaluation of safety and efficacy after procedure.

Materials and Methods

In the RTC practice adult bone marrow stem cell population with surface antigen CD133+ is applied for the treatment. The description of the stem cell generation is presented below. Bone marrow is aspirated primarily from the iliac crest (93,9% of cases) with preheparinized syringes. CD133+ bone marrow hematopoetic stem cells (BMSC) are isolated by magnetic separation with ferrite-conjugated antibody (Miltenyi CliniMacs System; Miltenyi Biotec, Bergisch Gladbach, Germany). Flow cytometry is performed to evaluate the purity and quality of the stem cell product. The median CD133+ BMSC dose was over 3,9×106 cells in 1 ml (95% CI 4,0-5,3). The stem cells are injected transepicardial during coronary-artery bypass surgery (CABG) or mitral valve surgery, before the aortic clamp is removed, or stand- alone application via mini-thoracotomy.

During last 14 years, after the first intramyocardial stem cell application in 2001 [6, 7], 223 patients have been treated in RTC. Currently the Multicenter Phase III clinical randomized placebo-control study is carried out [2], following terminated clinical studies Phase I and Phase II [10]. After studies closure all participants of mentioned trials are observed yearly lifelong within the registry program established in our center. In addition, the register contains information about patients with stem cells application after mitral valve replacement or reconstruction with or without CABG, patients with stand-along stem cells application and control groups treated with standard operations without stem cells injection. The registry program in RTC includes parameters for the evaluation of functional outcomes, such as left ventricle ejection fraction (LVEF), left ventricle end diastolic diameter (LVEDD) and left ventricle end systolic diameter (LVESD), measured by ultrasound system, 6-minute walk test, NT-proBNP, heart failure class (NYHA) and angina pectoris class (CSS). Moreover, registry contains such safety parameters as results of laboratory tests (Troponin, CK-MB, CK, CRP, Leucocytes), ECG, mortality rate and major adverse cardiovascular and cerebral events (MACCEs). MACCE is defined as an incidence of cardiac death, myocardial infarction, rehospitalization and intensive care stays due to cardiac events and percutaneous or surgical revascularization, acute heart failure, ventricular arrhythmias, postoperative implantation of defibrillators or resynchronization therapy and apoplexies. In addition, we study the events of new tumor formation, immune diseases and infections after stem cell transplantation procedure.

From 2001 to 2015 two hundred twenty three patients were followed with a total of 1152 patient years. General survival rate was about 67% up to 14 years. In the present paper we report an analysis of MACCEs and trans thoracic echocardiographic (TTE) data of 96 patients (n=73 cell therapy, n=23 control group) with coronary arterial disease and heart failure, treated with CABG with or without stem cells injections. Moreover, we stratified patient population into responders and non-responders based on change in global LVEF more than 5% at 12 month follow-up, reduction of LVEDD of more than –5 mm. “Absolute” responders were defined as patients who had an improvement of both parameters.

Results

Two different tendencies for the short-term and long-term outcomes were revealed after analysis of heart functional parameters collected in the register before the procedure, 12 months follow-up and further yearly up to 14 years. After 12 months follow-up no significant difference in the efficiency of treatment was demonstrated in stem cell (n=73) versus control groups (n=23). The results listed that LVEF was increased by +5,3% in the stem cells group (35,7% ± 10,0 to 41,0±9,0%; P<0,001), and by +4.7% in the matched control group (37,2%±2,0 to 41,9±2,0; P=0,05), LVEDD decreased by –2,3 mm in the stem cell group (57,8±6,1 mm to 55,5±6,2 mm; P<0,001) and by -2,5 mm in the control group (59,4±1,2 mm to 56,9±1,1 mm; P=0,022) 12 months after procedure. At the same time, the positive functional effect after stem cells injection was continuing during 5 years, whereas in the control group (CABG-only) – only during 2 years. Particularly, ejection fraction in patients of stem cell group was 45,3±3,2% 5 years follow up versus 35,7%±10,0 before operation (P<0,05). In the control group the LVEF, on the contrary, returned back to baseline numbers after moderate peak at 2 years follow-up. These tendencies are demonstrated in the Fig 1. Such parameters as LVEDD and LVESD measured by TTE showed no significant changes after 1 year follow-up in both, stem cell and control groups (data not shown).

>+5% LVEF (N=96)	Responder (n=44)	Non-responder (n=52)	p#
MACCE N (%)	14 (32%)	25 (48%)	0,144
Mortality N (%)	5 (11%)	14 (27%)	0,073
>5 mm LVEDD (N=96)	Responder (n=31)	Non-responder (n=65)	P#
MACCE N (%)	13 (42%)	26 (40%)	0,999
Mortality N (%)	3 (10%)	16 (25%)	0,105
Combination of EF+LVEDD (N=96)	Responder (n=17)	Non-responder (n=79)	P#
MACCE N (%)	4 (24%)	35 (44%)	0,173
Mortality N (%)	0 ( 0%)	19 (24%)	0,020

Table 1. Mortality and MACCEs in responder and non-responder patient groups

Legend Table 1: # Fisher´s exact test.

Morality and MACCEs were analyzed up to 14 years. During this follow-up period 36 patients died: 28 from 114 (25%) patients in the CD133+ BMSC plus CABG group, and 8 from 36 (22%) in the CABG-only group (P=0,827). No significant difference in MACCEs between the treatment groups was observed: 45 (39%) events in the stem cell group versus 17 (47%) recorded events in the control group (P=0,442). The detailed analysis listed that cases of postoperative implantation of defibrillators or resynchronization therapy did not differ significant between stem cell and control group (17% versus 14%, P=0,799). New episodes of ventricular arrhythmias occurred in 13% and 11% cases in stem cell group versus control group (P=1,000). Moreover, the percentages of apoplexies were almost equal in stem cell and control groups (8,8% and 8,3% respectively, P=1,000) during 14 year follow-up. Another MACCEs including rehospitalization and intensive care stays due to cardiac events and percutaneous or surgical revascularization took place in 6,1% and 8% in the stem cell group and in 5,5% and 5,5% in control group, respectively. There were no cases of immune diseases in both groups. At the same time two patients (1,8%) have died from lung and bronchial cancer in stem cell group after 50 and 86 months follow up.

Characteristics at baseline	Responder (n=15)	Non-responder (n=58)	P
EF (%) (mean±SD)	27,6±9,2	38,4±9,3	<0,001*
LVEDD (mm) (mean±SD)	63,1±5,7	56,4±5,6	<0,001*
LVESD (mm) (mean±SD)	49,0±8,3	42,3±7,7	0,017*
NT-proBNP (pg/ml) Median (25%Q-75%Q)	2558 (1756-5180)	762 (453-2456)	0,102**
Number of stem cells (mean±SD)	3,8±2,3	4,7±3,2	0,311*
Diabetes N (%)	7 (47%)	24 (41%)	0,774#
Hypertonia N (%)	15 (100%)	54 (93%)	0,575#
Smoking N (%)	6 (40%)	15 (26%)	0,341#
Dyslipidemia N (%)	13 (87%)	49 (85%)	1,000#
Anti coagulantia N (%)	5 (33%)	23 (40%)	0,770#
Aspirin N (%)	4 (27%)	8 (14%)	0,253#
Beta blocker N (%)	11 (73%)	48 (83%)	0,467#
Statin N (%)	14 (93%)	47 (81%)	0,438#
Diuretic N (%)	11 (73%)	29 (50%)	0,148#
ACE inhibitor N (%)	10 (66%)	44 (76%)	0,516#

Table 2. Comparison between “absolute” responder/ non-responder patients. Pre-operative results.

Legend Table 2: # Fisher´s exact test, * Two-sample t-test, ** Mann-Whitney U test.

In addition, mortality rate and MACCE were used to found the most reliable outcome parameter in patient’s long term prognosis. We compared rate of mortality and MACCE with changes in short-term functional outcomes: The Table 1 suggests that responders, who had an improvement of both functional parameters (LVEF and LVEDD) after 12 months follow up, had 0% mortality up to 14 years in comparison with patients, who improved only LVEF or LVEDD. In addition, 24% of deaths were noted in “absolute” non-responder group (P=0,020). Further, we compared the characteristics at a baseline between responders and non-responders to identify predictors of good response to stem cell therapy. The analysis revealed that patients who responded to CD133+ cell therapy had an average pre-operative lower LVEF (27 % versus 38%, P<0,001) and lager LVEDD and LVESD (63 mm and 49 mm versus 56 mm and 42 mm, P<0,001, P=0,017) compared to control group. In addition, NT-proBNP levels were higher in patients that responded to stem cell therapy (P=0,102). However, the numbers of CD133+ stem cells, concomitant diseases or given medication were not associated with the responsiveness of patients. This comparison of different baseline criteria for responders and non-responders is given in the Table 2.

Another sub –study was performed on registry database: the correlation between objective dates (changes in LVEF) and subjective dates (changes in Quality of life) was evaluated at 6 months follow up after stem cell injection. The study listed that 13% of patients had false-positive result or placebo effect as they reported an improvement in their physical condition, although LVEF did not increase after procedure. More interestingly, that patients, who reported negative changes (14%) after procedure, indeed had an impairment according to date of echo test.

Figure 1. Changes in LVEF during 5 years follow up in stem cell and control groups.

Legend Figure 1: *Paired t-test. The figure shows the ratio between LVEF before and after treatment with combination of CABG and stem cells injection or CABG- only. The results are represented with 1 year interval after therapy.

Discussion

The analysis of results of patients with ischemic disease and heart failure, who were observed in the frame of stem cell register in RTC, did not reveal significant difference in changes of functional outcomes between stem cell and control group 12 months follow up. In addition, we compared these data with results obtained in previous Phase II trial. On the contrary to the registry, Phase II study revealed a significant improvement in stem cells group compared to control group: LVEF increased from 37,4% ± 8,4% to 47,1% ± 8,3% in stem cell group compared to 37,9% ± 10,3% to 41,3% ± 9,1% in the CABG-only group at 6 months after the treatment (P=0,03) [Stamm et al. 2007]. We proposed that heterogeneity of patient’s population between Phase II study and the register is a basis of showed disparity. We came to the conclusion that registry data cannot substitute the results of randomized placebo-control clinical trials. Nevertheless, the evaluation of functional parameters during the time in one group or long-term analysis between treated and control groups recruited for one study can be provided.

Moreover, we have concluded that MACCEs are considerably important data in the register. The late probable complications including arrhythmias, infarctions, apoplexies, calcifications, tumors etc. can be monitored closely after stem cell application and their comparison with the control group can be carried out. Patient’s safety is a prerogative for any clinical trial in all the aspects of Good Clinical Practice. In addition, these data may be used for a standardized pharmacovigilance reporting to regulatory authorities. Therefore, MACCEs, which are representing main parts of safety evaluation process, could be selected as the most relevant parameter of the registry.

Another function of registry program can be applied for selecting patients with good response to stem cell therapy. It has been known for a while that the response to BMSC therapy varies with different subsets of patients [4, 5]. We stratified patient population into responders and non-responders based on change in global LVEF more than 5% at 12 month follow-up, reduction of LVEDD of more than -5 mm. “Absolute” responders were defined as patients who had an improvement of both parameters. Our study confirmed validity of this proposed criteria, since patients who were “absolute” responders after CD133+ BMSC injection showed no mortality during up to 14 years follow up (P=0,02). Moreover, our analysis revealed predictors for good response after stem cell therapy in patients with ischemic disease and heart failure. There are: LVEF below 30% and severe heart dilatation with a LVEDD over 60 mm. This observation was earlier confirmed by Wen Y. et al., [9] who demonstrated an enhanced improvement of ejection fraction after bone marrow-derived mononuclear cell therapy in patients with ischemic heart failure compared to patients with ischemic heart disease. In addition, the meta-analysis by Jeevanantham et al. [3] showed that stem cell treatment in patients with low baseline LVEF (less than 40%) resulted in greater improvement in LVESV and LVEDV (P=0,0004, P=0,01, respectively). We observed no influence of number of transplanted CD133+ BMSC (in the range of 0,5-20×106) as well as concomitant disease or given medication on the responsiveness of the patients. These findings are partly supported by work of Bai et al., who found no clear correlation between the number of intracoronary delivered BMSC and changes in LVEF [1]. To summarize, these results including pre-operative ejection fraction and heart dimension, can be applied as criteria for the selection of suitable for stem cell therapy candidates.

Conclusion

The necessity of common stem cell registry program is increasing with expansion of stem cell therapy. New tasks including observance of Good Clinical Practice and pharmacovigilance issue the new challenges to stem cell treatment. The analysis of the registry program in the Regeneration and Translation Center for cardiac stem cell therapy showed, that the register can answer to many introduced questions. Particularly, yearly MACCE registrations allow following late probable complications after therapy and comparing them to control group. Moreover, MACCE can be a basis for standardized pharmacovigilance reporting to regulatory authorities. In addition, registry program could help to improve patient selection to stem cell therapy and define patients who did not respond to it. Nevertheless, the registry by itself cannot replace randomized clinical trials. However, registry data can be used for long term efficiency and safety evaluation in standardized patient groups. To complement these data a Phase III randomized double-blinded PERFECT trial (NCT00950274) was installed in 2009 and will be finished 2016.

Funding Sources

This work was supported by German Federal Ministry of Education and Research (Grant Nr. 1316159).

Conflict of interest: none declared.

References

Bai Y., Sun T.; Ye P. Age, gender and diabetic status are associated with effects of bone marrow cell therapy on recovery of left ventricular function after acute myocardial infarction: a systematic review and meta-analysis. Ageing. Res. Rev 2010; 9: 418-423.
Donndorf P., Kaminski A., Tiedemann G., Kundt G., Steinhoff G. Validating intramyocardial bone marrow stem cell therapy in combination with coronary artery bypass grafting, the PERFECT Phase III randomized multicenter trial: study protocol for a randomized controlled trial. Trials 2012; 3: 99
Jeevanantham V., Butler M., Saad A., Abdel-Latif A., Zuba-Surma E. K., Dawn B. Adult bone marrow cell therapy improves survival and induces long-term improvement in cardiac parameters. A systematic review and meta-analysis. Circulation 2012; 126: 551-568.
Panovsky R., Vasku A., Meluzin J., Kaminek M., Mayer J., Janousek S., Kincl V., Groch L., Navratil M. Association of polymorphisms of zinc metalloproteinases with clinical response to stem cell therapy. Herz 2010; 35: 309-316.
Rodrigo S. F., van Ramshorst J., Mann, I., Leong D. P., Cannegieter S. C., Younis I. A., Dibbets-Schneider P., de Roos A., Fibbe W. E., Zwaginga J. J., Bax J. J., Schalij M. J., Beeres S. L., Atsma D. E. Predictors of response to intramyocardial bone marrow cell treatment in patients with refractory angina and chronic myocardial ischemia. Inter. J. Cardiol. 2014; 175: 539–544.
Stamm C., Westphal B., Kleine H. D., Petzsch M., Kittner C., Klinge H., Schümichen C., Nienaber C. A., Freund M., Steinhoff G. Autologous bone-marrow stem-cell transplantation for myocardial regeneration. Lancet 2003; 361: 45-46.
Stamm C., Kleine H. D., Choi Y. H., Dunkelmann S., Lauffs J. A., Lorenzen B., David A., Liebold A., Nienaber C., Zurakowski D., Freund M., Steinhoff G. Intramyocardial delivery of CD133+ bone marrow cells and coronary artery bypass grafting for chronic ischemic heart disease: safety and efficacy studies. J. Thorac. Cardiovasc. Surg. 2007; 133: 717-725.
Strauer B.E., Brehm M., Zeus T., Gattermann N., Hernandez A., Sorg R. V., Kögler G., Wernet P. Intracoronary, human autologous stem cell transplantation for myocardial regeneration following myocardial infarction. Dtsch Med Wochenschr 2001; 126: 932-938.
Wen Y., Chen B., Wang C., Ma X., Gao Q. Bone marrow-derived mononuclear cell therapy for patients with ischemic heart disease and ischemic heart failure. Expert Opin. Biol. Ther. 2012; 12: 1563-1573.
Yerebakan C., Kaminski A., Westphal B., Donndorf P., Glass A., Liebold A., Stamm C., Steinhoff G. Impact of preoperative left ventricular function and time from infarction on the long-term benefits after intramyocardial CD133(+) bone marrow stem cell transplant. J. Thorac. Cardiovasc. Surg. 2011; 142: 1530-1539. e3.

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Introduction

Materials and Methods

Results

>+5% LVEF (N=96)	Responder (n=44)	Non-responder (n=52)	p#
MACCE N (%)	14 (32%)	25 (48%)	0,144
Mortality N (%)	5 (11%)	14 (27%)	0,073
>5 mm LVEDD (N=96)	Responder (n=31)	Non-responder (n=65)	P#
MACCE N (%)	13 (42%)	26 (40%)	0,999
Mortality N (%)	3 (10%)	16 (25%)	0,105
Combination of EF+LVEDD (N=96)	Responder (n=17)	Non-responder (n=79)	P#
MACCE N (%)	4 (24%)	35 (44%)	0,173
Mortality N (%)	0 ( 0%)	19 (24%)	0,020

Table 1. Mortality and MACCEs in responder and non-responder patient groups

Legend Table 1: # Fisher´s exact test.

Characteristics at baseline	Responder (n=15)	Non-responder (n=58)	P
EF (%) (mean±SD)	27,6±9,2	38,4±9,3	<0,001*
LVEDD (mm) (mean±SD)	63,1±5,7	56,4±5,6	<0,001*
LVESD (mm) (mean±SD)	49,0±8,3	42,3±7,7	0,017*
NT-proBNP (pg/ml) Median (25%Q-75%Q)	2558 (1756-5180)	762 (453-2456)	0,102**
Number of stem cells (mean±SD)	3,8±2,3	4,7±3,2	0,311*
Diabetes N (%)	7 (47%)	24 (41%)	0,774#
Hypertonia N (%)	15 (100%)	54 (93%)	0,575#
Smoking N (%)	6 (40%)	15 (26%)	0,341#
Dyslipidemia N (%)	13 (87%)	49 (85%)	1,000#
Anti coagulantia N (%)	5 (33%)	23 (40%)	0,770#
Aspirin N (%)	4 (27%)	8 (14%)	0,253#
Beta blocker N (%)	11 (73%)	48 (83%)	0,467#
Statin N (%)	14 (93%)	47 (81%)	0,438#
Diuretic N (%)	11 (73%)	29 (50%)	0,148#
ACE inhibitor N (%)	10 (66%)	44 (76%)	0,516#

Table 2. Comparison between “absolute” responder/ non-responder patients. Pre-operative results.

Legend Table 2: # Fisher´s exact test, * Two-sample t-test, ** Mann-Whitney U test.

Figure 1. Changes in LVEF during 5 years follow up in stem cell and control groups.

Discussion

Conclusion

Funding Sources

This work was supported by German Federal Ministry of Education and Research (Grant Nr. 1316159).

Conflict of interest: none declared.

References

Bai Y., Sun T.; Ye P. Age, gender and diabetic status are associated with effects of bone marrow cell therapy on recovery of left ventricular function after acute myocardial infarction: a systematic review and meta-analysis. Ageing. Res. Rev 2010; 9: 418-423.
Donndorf P., Kaminski A., Tiedemann G., Kundt G., Steinhoff G. Validating intramyocardial bone marrow stem cell therapy in combination with coronary artery bypass grafting, the PERFECT Phase III randomized multicenter trial: study protocol for a randomized controlled trial. Trials 2012; 3: 99
Jeevanantham V., Butler M., Saad A., Abdel-Latif A., Zuba-Surma E. K., Dawn B. Adult bone marrow cell therapy improves survival and induces long-term improvement in cardiac parameters. A systematic review and meta-analysis. Circulation 2012; 126: 551-568.
Panovsky R., Vasku A., Meluzin J., Kaminek M., Mayer J., Janousek S., Kincl V., Groch L., Navratil M. Association of polymorphisms of zinc metalloproteinases with clinical response to stem cell therapy. Herz 2010; 35: 309-316.
Rodrigo S. F., van Ramshorst J., Mann, I., Leong D. P., Cannegieter S. C., Younis I. A., Dibbets-Schneider P., de Roos A., Fibbe W. E., Zwaginga J. J., Bax J. J., Schalij M. J., Beeres S. L., Atsma D. E. Predictors of response to intramyocardial bone marrow cell treatment in patients with refractory angina and chronic myocardial ischemia. Inter. J. Cardiol. 2014; 175: 539–544.
Stamm C., Westphal B., Kleine H. D., Petzsch M., Kittner C., Klinge H., Schümichen C., Nienaber C. A., Freund M., Steinhoff G. Autologous bone-marrow stem-cell transplantation for myocardial regeneration. Lancet 2003; 361: 45-46.
Stamm C., Kleine H. D., Choi Y. H., Dunkelmann S., Lauffs J. A., Lorenzen B., David A., Liebold A., Nienaber C., Zurakowski D., Freund M., Steinhoff G. Intramyocardial delivery of CD133+ bone marrow cells and coronary artery bypass grafting for chronic ischemic heart disease: safety and efficacy studies. J. Thorac. Cardiovasc. Surg. 2007; 133: 717-725.
Strauer B.E., Brehm M., Zeus T., Gattermann N., Hernandez A., Sorg R. V., Kögler G., Wernet P. Intracoronary, human autologous stem cell transplantation for myocardial regeneration following myocardial infarction. Dtsch Med Wochenschr 2001; 126: 932-938.
Wen Y., Chen B., Wang C., Ma X., Gao Q. Bone marrow-derived mononuclear cell therapy for patients with ischemic heart disease and ischemic heart failure. Expert Opin. Biol. Ther. 2012; 12: 1563-1573.
Yerebakan C., Kaminski A., Westphal B., Donndorf P., Glass A., Liebold A., Stamm C., Steinhoff G. Impact of preoperative left ventricular function and time from infarction on the long-term benefits after intramyocardial CD133(+) bone marrow stem cell transplant. J. Thorac. Cardiovasc. Surg. 2011; 142: 1530-1539. e3.

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"html" } ["DESCRIPTION"]=> string(0) "" ["VALUE_ENUM"]=> NULL ["VALUE_XML_ID"]=> NULL ["VALUE_SORT"]=> NULL ["~VALUE"]=> array(2) { ["TEXT"]=> string(80) "

Юлия Нестерук, Густав Штайнхофф

Референтный и трансляционный центр терапии сердца стволовыми клетками, Департамент кардиохирургии, медицинский факультет,Университет Ростока, Росток, Германия

Резюме

Стандартизация терапии стволовыми клетками требует методологии при оценке безопасности и эффективности лечения, a также длительное наблюдение за пациентами. Для выполнения перечисленного, в центре Регенеративной и Трансляционной медицины города Ростока, Германия, 14 лет назад была организована программа по долгосрочному наблюдению за больными после терапии стволовыми клетками и соответствующий регистр. Этот регистр содержит данные параметров для оценки эффективности и безопасности метода, в том числе основные церебральные и кардиальные события (MACCEs). Регистр включает 223 пациента (1152 пациент/года), которые наблюдаются каждый год пожизненно после имплантации стволовых клеток или в контрольной группе. Настоящее исследование выполнено на 96 больных ишемической болезнью сердца с сердечной недостаточностью: 73 больных после введения стволовых клеток во время операции аорто-коронарного шунтирования (АКШ) и 23 больных контрольной группы (только АКШ без стволовых клеток). Проведенный анализ выявил, что, по средствам регистрации основных церебральных и кардиальных событий (MACCE), достигается оценка возможных поздних и неожидаемых осложнений после терапии, что является необходимым при соблюдении принципов хорошей клинической практики (Good clinical practice). Тем не менее, регистр не может заменить собой рандомизированные клинические исследования, так как популяция больных в нем является гетерогенной. Однако данные регистра могут быть использованы для долгосрочной оценки безопасности и эффективности в стандартизованных группах больных. Кроме того, регистр помогает улучшить отбор больных для лечения стволовыми клетками и выявить пациентов, которые не отвечают на терапию. Введение единого обязательного регистра для всех центров, занимающихся терапией стволовыми клетками, является оправданным.

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Julia Nesteruk, Gustav Steinhoff

Reference and Translation Centre for Cardiac Stem Cell Therapy (RTC), Department of Cardiac Surgery, Medical Faculty, University of Rostock, Rostock, Germany.

Abstract

Standardization of stem cell therapy requires application of appropriate methods to evaluate safety and efficacy, including long-term pharmacovigilance. To accomplish this objective, a long-term registry program was installed in the Regeneration and Translation Center for cardiac stem cell therapy in Rostock, Germany 14 years ago. The registry program in RTC contains parameters for the evaluation of functional outcomes and safety including major adverse cardiovascular and cerebral events (MACCEs). The register follows 223 patients with a total of 1152 patient years, who are observed yearly lifelong after stem cell application or as a control groups. The present study was done in 96 patients (n=73 cell therapy, n=23 control group) with coronary arterial disease and heart failure, treated with coronary artery bypass operation with or without stem cells injections. The analysis revealed, that owing to yearly MACCE registrations, register allow that late probable complications are carefully followed as well as unexpected complication reported, which is required to ensure patient’s safety as the main aspect of Good Clinical Practice. Nevertheless, the registry by itself cannot substitute randomized clinical trials, as patient’s cohort is heterogeneous. However, registry data can be used for long term efficiency and safety evaluation in standardized patient groups. Furthermore, registry program could help to improve patient selection revealing predictors of good response and define patients who did not respond to the stem cell therapy. It is expedient to establish an obligatory common registry program for all centers, carrying stem cell studies.

Julia Nesteruk, Gustav Steinhoff

Abstract

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Reference and Translation Centre for Cardiac Stem Cell Therapy (RTC), Department of Cardiac Surgery, Medical Faculty, University of Rostock, Rostock, Germany.

Юлия Нестерук, Густав Штайнхофф

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Для выполнения перечисленного, в центре Регенеративной и Трансляционной медицины города Ростока, Германия, 14 лет назад была организована программа по долгосрочному наблюдению за больными после терапии стволовыми клетками и соответствующий регистр. Этот регистр содержит данные параметров для оценки эффективности и безопасности метода, в том числе основные церебральные и кардиальные события (MACCEs). Регистр включает 223 пациента (1152 пациент/года), которые наблюдаются каждый год пожизненно после имплантации стволовых клеток или в контрольной группе. Настоящее исследование выполнено на 96 больных ишемической болезнью сердца с сердечной недостаточностью: 73 больных после введения стволовых клеток во время операции аорто-коронарного шунтирования (АКШ) и 23 больных контрольной группы (только АКШ без стволовых клеток). Проведенный анализ выявил, что, по средствам регистрации основных церебральных и кардиальных событий (MACCE), достигается оценка возможных поздних и неожидаемых осложнений после терапии, что является необходимым при соблюдении принципов хорошей клинической практики (Good clinical practice). Тем не менее, регистр не может заменить собой рандомизированные клинические исследования, так как популяция больных в нем является гетерогенной. Однако данные регистра могут быть использованы для долгосрочной оценки безопасности и эффективности в стандартизованных группах больных. Кроме того, регистр помогает улучшить отбор больных для лечения стволовыми клетками и выявить пациентов, которые не отвечают на терапию. Введение единого обязательного регистра для всех центров, занимающихся терапией стволовыми клетками, является оправданным." 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Резюме

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Introduction

Recently quantification of measurable residual disease (MRD) has been increasingly-used to determine whether a person with acute myeloid leukaemia (AML) in 1st complete remission should receive a haematopoietic cell transplant. But how accurate are results of MRD-testing in this setting in predicting likelihood of leukaemia-relapse? I consider this issue in this commentary. I will provide data indicating results of MRD-testing in this setting are associated with substantial rates of false-negative and -positive predictions. The level of tolerance to be wrong and level of precision required or desired determine how useful results of MRD-testing are in deciding whether or not to recommend a person with AML in 1st complete remission should receive a haematopoietic cell transplant.

There are 2 major determinants of accuracy of results of MRD-testing: (1) adequacy of sampling (sometime referred to as sampling-error); and (2) precision of the MRD-testing being used reflecting sensitivity and specificity of the assay.

Predicting leukemia relapse in a subject is different than predicting leukaemia relapse in a cohort

Most analyses of the accuracy of MRD-testing are on the cohort level. Typically, an outcome such as cumulative incidence of leukaemia-relapse, relapse-free survival or survival is compared between cohorts of subjects with a positive or negative MRD-test result. Almost all of these studies report a greater risk of leukaemia-relapse in the cohort with a positive MRD-test result.

This association is often confirmed in multivariable regression analyses. Frequently results of MRD-testing are a stronger predictor of leukaemia-relapse than other leukaemia-associated variables such as age, gender, cytogenetic and/or molecular risk cohort. This is not surprising and indicates the predictive value of results of MRD-testing. However, more detailed analyses of hazards of leukaemia relapse indicate most of the difference in leukaemia relapse risk occurs within the 1st few months after the MRD-test is done. Afterwards, hazards of leukaemia relapse risk are similar. Reasons for this non-proportional hazard of leukaemia relapse relates primarily of residual numbers of leukaemia cells and are confounded with sampling error.

It is important to consider that predicting leukaemia relapse in a person is a different challenge than predicting leukaemia relapse in a cohort. Risk predictions of cohorts are typically given as point estimates with confidence intervals. Often these confidence intervals of cohorts are wide and may overlap even when the p-value for trend is significant. The consequence, for example, is some subjects in a favourable risk cohort may have a worse prognosis than some subjects in an unfavourable risk cohort. This observation should begin to caution the reader to the hazards of apply cohort predictions to predictions in cohort members. For example, although a cohort may have a 30 percent risk of leukaemia relapse, no member of the cohort has a 30 percent risk. The individual risk is, in contrast, a binary, leukaemia-relapse or no leukaemia-relapse, 0 or 100 percent.

Loss of predictive power with time post-treatment

Most of the variables correlated with outcomes of people with AML such age, gender, WBC and/or cytogenetic or molecular risk cohort are most powerful at diagnosis. This is because they are associated with likelihood of achieving complete remission. One a person achieves complete remission the prognostic power of these variables decreases. Furthermore, most variables are also associated with a risk of early leukaemia-relapse in persons achieving complete remission. Consequently, the variables are less powerful in a person remaining in remission at 3-4 months when a transplant decision is typically made.

Unidentified prognostic variables and chance

A key question is how much of the variance associated with outcomes in a population with AML are explained by known prognostic variables? This question is typically analyzed using a receiver-operator characteristic (ROC) curve and the C-statistic derived from this curve. A C-statistic of 0.5 implies the variable or combination of variables has no predictive value whereas a C-statistic of 1,0 implies perfect prediction. When the combination prognostic variables typically used in AML analyses is applied to persons with AML in complete remission for 3-4 months the C-statistic is about 0,7. This means prediction accuracy is better than random but far from perfect.

Why is this so? There are several possible answers but 2 mostly-likely are: (1) impact of potentially knowable but currently unknown variables; and (2) chance. Most scientists want to believe we can explain all uncertainty. The implication is we will eventually find other prognostic variables which allow us greater predictive precision. However, the expectation that all variance will be explainable is unlikely. This is because, much to most scientists’ dismay, stochastic events are important determinants of outcomes of therapy-interventions. Failure to acknowledge this fact is a major obstacle to understanding and accepting limitations of prediction models. Put otherwise, reality is the leading cause of stress amongst those in touch with it. (Jane Wagner.)

Sampling error

Most MRD-tests used in AML have only modest levels of sensitivity and specificity (see below). However, even a perfect MRD-test (100 percent sensitivity, 100 percent specificity) will have limited precision in predicting leukaemia-relapse in persons with AML in 1st complete remission. This is because of unavoidable sampling error. For example, it is common to use a 5-10 ml sample of blood or bone marrow for MRD-testing. This is probably OK when there are many leukaemia cells in the body (say 10E+11) i. e. every sample, even small sample volumes, are likely to contain ≥1 leukaemia cells. If so, a perfect MRD-test will detect them unambiguously with no false-negative or –positive results. (An unambiguous MRD-test result is different than a correct prediction.) However, when numbers of leukaemia cells in the blood and bone marrow decrease as a result of induction and post-remission therapies the MRD-test result will be a false-negative because there may not be a leukaemia cell in the sample whereas there may be many in the person being sampled. The probability of having ≥1 leukaemia cells in a small sample is reflected in a Poisson probability distribution (Figure 1). Examination of Figure 1 shows at low frequencies of leukaemia cells in a person, say 10E+3, there is a substantial probability there will be no a leukaemia cell in a small sample resulting in a false-negative MRD-test result. This unavoidable sampling error limitation is further confounded by the high likelihood leukaemia cells, and especially those with the biological ability to cause leukaemia relapse, are uniformly-distributed in the blood and bone marrow. The impact would be to increase the likelihood of false-negative MRD-test results. Naturally this error is confounded when a MRD-test has less than perfect sensitivity and/or specificity. An example of a substantial rate of false-negative MRD-test results is shown in Figure 2. This approximately 30 percent rate of false-negative MRD-test results in persons with AML in 1st complete remission after completing consolidation therapy likely results from a combination of sampling error and an imperfect M-test (see below).

Figure 1. Increased probability of false-negative MRD-test from a small sample of blood or bone marrow when numbers leukaemia cells in a person is decreased by therapy.

Figure 2. False-negative MRD-test results in persons with AML in 1st complete remission after completing consolidation therapy. About 30% of persons with a negative MRD-test result had leukaemia relapse. This is likely the result of sampling error and imperfect MRD-test sensitivity (from Terwijn et al., 2013).

Imperfect MRD-test sensitivity and specificity

It is unlikely that any MRD-test in AML can have 100 percent sensitivity and 100 percent specificity. For example, it is clear not every AML cell has the biological capability to cause leukaemia-relapse. Presently, we lack sensitive tests to distinguish leukaemia cells which can and cannot cause leukaemia relapse. The obvious consequence of this lack of discrimination is a high likely likelihood of false-positive MRD-test results. For example, a MRD-test which detects an AML cell which cannot cause leukaemia relapse will result in a false-positive MRD-test. Figure 3 shows data from the same study shown in Figure 2 indicating an about 30 percent frequency of false-positive MRD-test results.

Figure 3. False-positive MRD-test results in persons with AML in 1st complete remission after completing consolidation therapy. About 30% of persons with a positive MRD-test result did not have leukaemia relapse during the observation interval. This is likely the result of imperfect MRD-test specificity (from Teijwin et al., 2013).

Complexity of AML

AML is a complex neoplasm with considerable genetic and clonal complexity at diagnosis and even more so when there is leukaemia relapse. There is substantial genotypic diversity between people with AML. Data from whole exome and whole genome sequencing indicate every case of AML analyzed at diagnosis is genotypically unique; no 2 share an identical mutational profile. This makes it difficult or unlikely there can be a uniform genotype-based MRD-test in newly-diagnosed persons with AML. This heterogeneity is further increased at relapse between and within persons with AML. The situation with phenotypic analyses is equally complex. In addition to phenotypic diversity between person with AML considerable data suggest phenotypic diversity with a person with AML at diagnosis and especially at relapse. These considerations make it unlikely tests for MRD in persons with AML can achieve the specificity of MRD tests in acute lymphoid leukaemia (ALL) where the usual testing target is the B- or T-cell clone and not the neoplasms. We can have some appreciation of the complexity of MRD-testing in AML if we consider precision of MRD-testing in chronic myeloid leukaemia (CML) a much simpler neoplasm caused by 1 mutation (BCR/ABL) and for which we have highly sensitive and specific mRNA-based tests for MRD. Here the rate of false-negative MRD tests in the context of stopping imatinib therapy is about 60 percent (Figure 4). This situation is further complicated by recent reports of the detection of mutations previously thought to be typical of AML in normal, older persons not developing AML in their lifetime. Examples include mutations in DMNT3A, TET2 and IDH1 and IDH2. If these mutations were used in MRD-testing their lack of specificity for AML would result in a substantial rate of false-postive MRD-test results.

Figure 4. Rate of false-negative results in persons with CML discounting imatinib after having a negative MRD-test for BCR/ABL. The about 60% false-negative rate reflects imperfect sensitivity of the MRD-test.

Even accurate results may not be actionable

For results of an MRD-test to be clinically-useful the results must be accurate and actionable. For example, if one were to use a positive MRD-test as the basis for recommending a haematopoietic cell transplant one would need convincing data doing a transplant would result in a better outcome that an alternate such as no further therapy or more conventional chemotherapy. Proof of efficacy can only be obtained in a randomized trial in which persons with a positive MRD-test result are assigned to a transplant or an alternate. No such study is reported. However, a study underway in the UK may inform the question of whether a positive MRD-test result is actionable.

Conclusions

Although results of MRD-testing is correlated with probability of leukaemia relapse in cohorts of people with AML in 1st complete remission there are substantial barriers to applying results of MRD-testing to individual therapy recommendations. For reasons discussed a positive MRD-test result is likely to be wrong in 1 in 3 instances. Similarly, a negative MRD-test result is likely to be wrong in 1 of 3 instances. Thus, using MRD-test results as the basis for recommending a transplant to someone with AML in 1st remission requires a high tolerance level for being wrong. This is is especially concerning in persons with a positive MRD-test about 25-35 percent are already cured and cannot benefit but can be substantially harmed by a transplant. Further, we presently lack convincing data the adverse prognostic impact of a positive MRD-test can be reversed by doing a transplant. I hope this commentary will cause physicians to more carefully weigh results of MRD-testing in their prognostic metric. Comments welcome.

Acknowledgement

RPG acknowledges support from the National Institute of Health Research (NIHR) Biomedical Research Centre funding scheme.

References

Terwijn M, van Putten WL, Kelder A, van der Velden VH, Brooimans RA, Pabst T, et al. High prognostic impact of flow cytometric minimal residual disease detection in acute myeloid leukemia: data from the HOVON/SAKK AML 42A study. J Clin Oncol. 2013 Nov 1; 31 (31): 3889-3897.

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Introduction

Predicting leukemia relapse in a subject is different than predicting leukaemia relapse in a cohort

Loss of predictive power with time post-treatment

Unidentified prognostic variables and chance

Sampling error

Figure 1. Increased probability of false-negative MRD-test from a small sample of blood or bone marrow when numbers leukaemia cells in a person is decreased by therapy.

Imperfect MRD-test sensitivity and specificity

Complexity of AML

Even accurate results may not be actionable

Conclusions

Acknowledgement

RPG acknowledges support from the National Institute of Health Research (NIHR) Biomedical Research Centre funding scheme.

References

Terwijn M, van Putten WL, Kelder A, van der Velden VH, Brooimans RA, Pabst T, et al. High prognostic impact of flow cytometric minimal residual disease detection in acute myeloid leukemia: data from the HOVON/SAKK AML 42A study. J Clin Oncol. 2013 Nov 1; 31 (31): 3889-3897.

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Однако насколько точны результаты тестирования МОБ в этой ситуации для прогнозирования вероятности рецидива лейкоза? Имеются две основные детерминанты точности результатов: (1) адекватность (или ошибка) сбора биоматериала, и (2) точность тестирования МОБ, в плане чувствительности и специфичности диагностического метода. Так, например, большинство оценок точности тестирования МОБ проводится на уровне когорт пациентов. Обычно исходы оценивают в виде кумулятивной частоты рецидивов лейкоза и безрецидивной выживаемости для МОБ-позитивных и негативных пациентов. Проблема, в частности, состоит в том, что некоторые больные из благоприятной группы риска могут иметь худший прогноз, нежели некоторые лица из неблагоприятной группы риска. Следует иметь в виду возможность утраты прогностического потенциала со временем после лечения, т.е. переменные, определяющие риск, имеют меньшую силу у человека в ремиссии в течение 3-4 мес., в тот срок, когда решается вопрос о трансплантации. Дополнительные проблемы возникают из-за невыясненных прогностических факторов. В этих случаях результаты ROC-анализа показывают значительное снижение точности прогнозирования рецидива, что связано с различными стохастическими событиями, определяющими возможные исходы применяемой терапии. Кроме того, даже вполне совершенный тест на наличие МОБ со 100%-ной чувствительностью и 100%-ной специфичностью будет иметь ограничения по точности прогнозирования рецидивов лейкоза у лиц с ОМЛ в 1-й полной ремиссии. Это связано с неизбежной ошибкой сбора материала, которая затем еще более искажает результат ввиду высокой вероятности лейкозных клеток и, в особенности, клеток – источников рецидива в крови и костном мозге. Их влияние может быть связано с учащением ложно-негативных результатов теста на МОБ. Кроме того, ОМЛ является заболеванием со сложным генетическим составом и, в еще большей мере – в рецидиве заболевания. Недавно было показано, что некоторые маркерные мутации, ранее считавшиеся типичными для ОМЛ (например, мутации DMNT3A, TET2 и IDH), обнаруживают также у здоровых лиц старших возрастов. Их недостаточная специфичность при ОМЛ приводила бы к значительному снижению частоты ложно-позитивных результатов тестирования МОД. В заключение, хотя результаты тестирования МОБ коррелируют с вероятностью рецидива в группах больных АМЛ в первой полной ремиссии, имеются существенные объективные препятствия к использованию результатов оценки МОБ для рекомендаций по индивидуальной терапии. 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Гэйл" ["TYPE"]=> string(4) "html" } ["DESCRIPTION"]=> string(0) "" ["VALUE_ENUM"]=> NULL ["VALUE_XML_ID"]=> NULL ["VALUE_SORT"]=> NULL ["~VALUE"]=> array(2) { ["TEXT"]=> string(46) "

Роберт П. Гэйл

Центр гематологических исследований, отделение экспериментальной медицины, департамент медицины, Лондонский Имперский колледж, Лондон, Великобритания

Резюме

Количественная оценка определяемой остаточной болезни (МОБ) все чаще используется для решения о том, должна ли проводиться пересадка кроветворных клеток больному с острым миелоидным лейкозом (ОМЛ) в первой полной ремиссии. Однако насколько точны результаты тестирования МОБ в этой ситуации для прогнозирования вероятности рецидива лейкоза? Имеются две основные детерминанты точности результатов: (1) адекватность (или ошибка) сбора биоматериала, и (2) точность тестирования МОБ, в плане чувствительности и специфичности диагностического метода.

Так, например, большинство оценок точности тестирования МОБ проводится на уровне когорт пациентов. Обычно исходы оценивают в виде кумулятивной частоты рецидивов лейкоза и безрецидивной выживаемости для МОБ-позитивных и негативных пациентов. Проблема, в частности, состоит в том, что некоторые больные из благоприятной группы риска могут иметь худший прогноз, нежели некоторые лица из неблагоприятной группы риска. Следует иметь в виду возможность утраты прогностического потенциала со временем после лечения, т.е. переменные, определяющие риск, имеют меньшую силу у человека в ремиссии в течение 3-4 мес., в тот срок, когда решается вопрос о трансплантации. Дополнительные проблемы возникают из-за невыясненных прогностических факторов. В этих случаях результаты ROC-анализа показывают значительное снижение точности прогнозирования рецидива, что связано с различными стохастическими событиями, определяющими возможные исходы применяемой терапии. Кроме того, даже вполне совершенный тест на наличие МОБ со 100%-ной чувствительностью и 100%-ной специфичностью будет иметь ограничения по точности прогнозирования рецидивов лейкоза у лиц с ОМЛ в 1-й полной ремиссии. Это связано с неизбежной ошибкой сбора материала, которая затем еще более искажает результат ввиду высокой вероятности лейкозных клеток и, в особенности, клеток – источников рецидива в крови и костном мозге. Их влияние может быть связано с учащением ложно-негативных результатов теста на МОБ. Кроме того, ОМЛ является заболеванием со сложным генетическим составом и, в еще большей мере – в рецидиве заболевания. Недавно было показано, что некоторые маркерные мутации, ранее считавшиеся типичными для ОМЛ (например, мутации DMNT3A, TET2 и IDH), обнаруживают также у здоровых лиц старших возрастов. Их недостаточная специфичность при ОМЛ приводила бы к значительному снижению частоты ложно-позитивных результатов тестирования МОД.

В заключение, хотя результаты тестирования МОБ коррелируют с вероятностью рецидива в группах больных АМЛ в первой полной ремиссии, имеются существенные объективные препятствия к использованию результатов оценки МОБ для рекомендаций по индивидуальной терапии.

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Robert Peter Gale

Haematology Research Centre, Division of Experimental Medicine, Department of Medicine, Imperial College London, London, UK.

Summary

Quantification of measurable residual disease (MRD) is increasingly used to determine whether a person with acute myeloid leukaemia (AML) in the 1st complete remission should receive a haematopoietic cell transplant. But how accurate are results of MRD-testing in this setting in predicting likelihood of leukaemia-relapse? There are 2 major determinants of accuracy of results of MRD-testing: (1) adequacy of sampling (sometime referred to as sampling-error); and (2) precision of the MRD-testing being used reflecting sensitivity and specificity of the assay.

Most analyses of the accuracy of MRD-testing are on the cohort level. E.g., most analyses of the accuracy of MRD-testing are on the cohort level. Typically, an outcome such as cumulative incidence of leukaemia-relapse, relapse-free survival for MRD`+ vs MRD- cases. The problem, for example, is that some subjects in a favourable risk cohort may have a worse prognosis than some subjects in an unfavourable risk cohort. One should mean a loss of predictive power with time post-treatment, i. e., the risk-determining variables are less powerful in a person remaining in remission at 3-4 months when a transplant decision is typically made. Other questions arise due to unidentified prognostic variables. Under these conditions, the ROC analysis shows rather low prediction accuracy. This is because of different stochastic events determining probable outcomes of the therapy applied.

Moreover, even a perfect MRD-test (100 percent sensitivity, 100 percent specificity) will have limited precision in predicting leukaemia-relapse in persons with AML in 1st complete remission. This is because of unavoidable sampling error which is further confounded by the high likelihood of leukaemia cells, and especially those causing leukaemia relapse in the blood and bone marrow. This impact would be associated with more common false-negative MRD-test results.

Moreover, AML is a genetically complex neoplasm at diagnosis and even more at the leukaemia relapse. Recently, some marker mutations previously thought to be typical of AML have been found in normal, older persons (e. g., DMNT3A, TET2 and IDH mutations). Their lack of specificity for AML would result in a substantial rate of false-positive MRD-test results.

In conclusion, although results of MRD-testing are correlated with probability of relapse in cohorts of persons with AML in 1st complete remission, there are substantial barriers to applying results of MRD-testing to recommendations for individual therapy.

Robert Peter Gale

Summary

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Haematology Research Centre, Division of Experimental Medicine, Department of Medicine, Imperial College London, London, UK.

Роберт П. Гэйл

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"HTML" ["USER_TYPE_SETTINGS"]=> array(1) { ["height"]=> int(200) } ["HINT"]=> string(0) "" ["PROPERTY_VALUE_ID"]=> string(4) "1323" ["VALUE"]=> array(2) { ["TEXT"]=> string(5247) "<h2 class="Summery"> Резюме </h2> Количественная оценка определяемой остаточной болезни (МОБ) все чаще используется для решения о том, должна ли проводиться пересадка кроветворных клеток больному с острым миелоидным лейкозом (ОМЛ) в первой полной ремиссии. Однако насколько точны результаты тестирования МОБ в этой ситуации для прогнозирования вероятности рецидива лейкоза? Имеются две основные детерминанты точности результатов: (1) адекватность (или ошибка) сбора биоматериала, и (2) точность тестирования МОБ, в плане чувствительности и специфичности диагностического метода. Так, например, большинство оценок точности тестирования МОБ проводится на уровне когорт пациентов. Обычно исходы оценивают в виде кумулятивной частоты рецидивов лейкоза и безрецидивной выживаемости для МОБ-позитивных и негативных пациентов. Проблема, в частности, состоит в том, что некоторые больные из благоприятной группы риска могут иметь худший прогноз, нежели некоторые лица из неблагоприятной группы риска. Следует иметь в виду возможность утраты прогностического потенциала со временем после лечения, т.е. переменные, определяющие риск, имеют меньшую силу у человека в ремиссии в течение 3-4 мес., в тот срок, когда решается вопрос о трансплантации. Дополнительные проблемы возникают из-за невыясненных прогностических факторов. В этих случаях результаты ROC-анализа показывают значительное снижение точности прогнозирования рецидива, что связано с различными стохастическими событиями, определяющими возможные исходы применяемой терапии. Кроме того, даже вполне совершенный тест на наличие МОБ со 100%-ной чувствительностью и 100%-ной специфичностью будет иметь ограничения по точности прогнозирования рецидивов лейкоза у лиц с ОМЛ в 1-й полной ремиссии. Это связано с неизбежной ошибкой сбора материала, которая затем еще более искажает результат ввиду высокой вероятности лейкозных клеток и, в особенности, клеток – источников рецидива в крови и костном мозге. Их влияние может быть связано с учащением ложно-негативных результатов теста на МОБ. Кроме того, ОМЛ является заболеванием со сложным генетическим составом и, в еще большей мере – в рецидиве заболевания. Недавно было показано, что некоторые маркерные мутации, ранее считавшиеся типичными для ОМЛ (например, мутации DMNT3A, TET2 и IDH), обнаруживают также у здоровых лиц старших возрастов. Их недостаточная специфичность при ОМЛ приводила бы к значительному снижению частоты ложно-позитивных результатов тестирования МОД. В заключение, хотя результаты тестирования МОБ коррелируют с вероятностью рецидива в группах больных АМЛ в первой полной ремиссии, имеются существенные объективные препятствия к использованию результатов оценки МОБ для рекомендаций по индивидуальной терапии. " ["TYPE"]=> string(4) "html" } ["DESCRIPTION"]=> string(0) "" ["VALUE_ENUM"]=> NULL ["VALUE_XML_ID"]=> NULL ["VALUE_SORT"]=> NULL ["~VALUE"]=> array(2) { ["TEXT"]=> string(5131) "

Резюме

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Introduction

Both latent and overt polyomavirus infections are widely spread in human population [3]. Specifically, BK virus (BKV) is commonly detected in urine, blood leukocytes and plasma of the patients after renal transplantation, followed in some cases by clinically sound polyomavirus nephropathy [12].

Polyomaviruses are also activated following hematopoietic stem cell transplantation (HSCT), due to immune deficiency caused by transient hematopoietic aplasia and immunosuppressive treatment. E. g., BKV infection is associated with hemorrhagic cystitis in transplanted patients [13], whereas JC virus (JCV) positivity may be connected with increased risk of multifocal encephalopathy [2]. Moreover, some other cells and tissues bear specific receptors for JCV, thus providing additional targets for this pathogen [5].

Hence, the aim of our study was to evaluate time-dependent changes in BKV and JCV positivity in different clinical specimens in the patients undergoing HSCT procedure.

Materials and methods

Patients

From 2010 to 2013, we observed 87 patients (1 to 60 years old) with various hematological disorders including acute lymphoblastic leukemia (ALL, n=44), acute myeloblastic leukemia (AML, n=21), myelodysplastic syndrome (MDS, n=8), malignant lymphomas (n=6), aplastic anemias (n=4), inherited metabolic syndromes (n=3), chronic myelogenous leukemia (CML, n=1). All the patients underwent allogeneic HSCT. Myeloablative conditioning regimens were applied in 42% of cases. The patients received transplants from related HLA-compatible (12%), haploidentical (28%), or unrelated, HLA-compatible donors (60%). Bone marrow (BM) or peripheral blood stem cells (PBSC) were transplanted, respectively, in 52,5% and 47,5% of cases. Standard pre-emptive acyclovir treatment was administered to all patients. To prevent acute graft-versus-host disease (aGvHD), we used cyclosporin A and metothrexate.

Polyomavirus DNA screening

DNA diagnostics of ВКV and JCV in blood and urine was performed bi-weekly, using qualitative gene-specific PCR. The samples were taken up to 3 months, and, if possible, until 6 months post-HSCT. Polyomavirus DNA was assayed in peripheral blood leukocytes obtained from 0,5 mL of whole blood by lysis and repeated centrifugation, or in urinary cell specimens. In complicated cases, appropriate local samples were tested, i. e., cell pellets from urine, cerebrospinal fluid (CSF), broncho-alveolar lavage (BAL). In such cases, polyomavirus assays were administered by the attending doctor, usually for suspected cystitis, respiratory, or neurological disorders. DNA isolation from the cell lysates was performed with a standard sorbent technique («DNA-Sorb», Interlabservice, Moscow). BK virus DNA (VP1 gene) was detected by means of gene specific PCR [10]. The following primers were used: sense, 5’- aaa tcc att tta tct aat ata tg -3’ and antisense, 5’-ggc tta aag gag cat ga-3’ (amplicon length, 372 bp). JCV detection was based on a test system used elsewhere (7), using the following primers: sense, 5’-tattccaccaggattcccattc-3’. and antisense, 5’-gttcttggagacaccccctaca-3’ (amplicon length, 150 bp), purchased from Synthol, Moscow. DNA amplification was carried out in a standard master mixture with 5xPCR buffer, using Bio-Rad ICycler. CMV DNA was detected with a commercial test system (Interlabservice, Moscow).

PCR products were separated by means of electrophoresis in 2% agarose gel. Gel photographing, and identification of specific gene products was performed with “Bio-Rad” gel documentation system. Actual sensitivity of the BKV and JCV detection assays was, respectively, 100 and 200 gene copies/mL of the sample, as tested with appropriate quantitative controls.

Statistical methods

Statistical evaluation of the data was performed by means of STATISTICA 6.0 software, using χ2 and Student criteria. Correlation quotients and their significance were determined by Spearman criterion. The differences were considered significant at Р<0,05.

Results

Overall frequency of positive BKV tests in the patients during the entire observation period was 31% for blood, 72% for urine, 16% for CSF, and 21% for BAL samples (for a total of 663 clinical specimens). Appropriate rates for JCV were, respectively, 16%, 36%, 2% и 19%. As seen from these figures, BKV was more commonly found in urine than in other biological samples (p<0,0001). JCV was also rather common in urine (p<0,0001), but it was quite rare in CSF cells, despite of its proposed role in brain disorders [2].

Types of samples	Age of patients, years
Types of samples	1-10	11-20	21-30	31>
BKV blood	0,33+0,05 (96)	0,30+0,05 (83)	0,33+0,11 (23)	0,37+0,09 (27)
BKV urine	0,61+0,09 (31)	0,79+0,05 (75)	0,87+0,06* (31)	0,89+0,06* (27)
JCV blood	0,20+0,04 (97)	0,16+0,04 (83)	0,15+0,08 (20)	0,23+0,08 (26)
JCV urine	0,28+0,08 (29)	0,44+0,06 (71)	0,32+0,09 (28)	0,48+0,09 (27)

Total numbers of clinical samples are shown in parentheses *, difference against younger groups (<20 years old) is significant by p=0,04.

Table 1. Frequency of BKV and JCV-positive tests (M+m) in blood leukocytes and urinary cells for different age groups

In addition, there was a strong correlation between BK and JC virus occurrence in the samples (r=0,29, p=0,00001), especially, in urinary cells. Similar correlation for urine specimens was found between JCV and cytomegalovirus positivity (r=0,28, p=0,001)

We have also studied age-dependence of BKV and JCV prevalence in blood and urine (Table 1). BKV prevalence was about 60% in younger patients, with a moderate increase with patients’ age (at 30-40 years and older). Meanwhile, we did not reveal a significant age-dependent increase in JCV frequency.

As shown in Fig. 1, upon assessing post-HSCT changes of BKV prevalence in blood and urine, we have noted a sufficient increase of BKV incidence in urine, with a peak at 2nd month (from 43% to 87%, p<0,002), thus well correlating with high risk of hemorrhaghic cystitis within 100 days after HSCT.

Posttransplant changes of JCV prevalence in urine were quite similar to those of BKV dynamics (Fig.2), showing a significant increase within first months post-HSCT (from 9% be to 32-41% at 1-3 months, p=0,04). Noteworthy, the JC virus frequency was shown to peak in bronchoalveolar samples at 4 to 6 months post-transplant (6 positives of 16 samples, as compared to 0/19 at earlier terms, p=0,003).

We did not reveal any significant correlations between the positivity for BKV or JCV, and source of stem cell source (BM vs PBSC), and intensity of conditioning regimen. However, an increased urinary BKV incidence was noted in cases of haploidentical HSCT, as compared with HLA-compatible transplantation, respectively, 87% (33/38) vs. 70% (97/138), p=0,04.

Fig. 1. Incidence of BK virus-positive tests in blood (squares) and urine (triangles) after HSCT (M+m). Abscisse: terms after transplantation, months. Ordinate, frequency of BK-positive tests.

Fig. 2. Frequency of JCV-positive tests in blood (squares) and urine (triangles) after HSCT (M+m). Abscisse: terms after transplantation, months. Ordinate, frequency of JC-positive tests.

Discussion

BK-positivity of urine seems to be a regular event in severely immunocompromised patients after HSCT, thus confirming its involvement in post-treatment cystitis and other complications. Several studies aimed for searching interrelations between BK reactivation and hemorrhagic cystitis post-HSCT [1, 14]. Generally, BK-viruria is shown to be more common than viremia in posttransplant period. However, blood BK levels are also reported to be of a prognostic value for cystitis [8]. A study by Drew et al. [4] has shown that BK activation in patients of hemorrhagic cystitis mainly develops during the 3rd month post-HSCT, with even higher viral loads in urine than in blood plasma. In our experience, this time dynamics is replicated for the urinary samples in general group of HSCT patients.

The most possible reason of post-HSCT cystitis lays in well-known avidity of BKV for urotelium [16]. Our observation of higher BK viruria rates in haploidentical HSCT is in accordance with increased BK incidence in serum after mismatched renal transplantation [9].

Possible role of JC virus after HSCT remains less clear. E.g., it was rarely found in cerebrospinal fluid of our posttransplant patients, thus suggesting its marginal pathogenetic role in encephalopathies developing in some HSCT cases. Meanwhile, the JCV incidence is increasing in time-dependent manner both in urine (at 1-3 months post-HSCT).

Similarly, JC-positivity proved to be quite common in bronchoalveolar cells at 3-4 months after HSCT. Certainly, this finding needs confirmation with larger number of cases. However, we cannot exclude a possible involvement of JCV into respiratory disorders. JCV is shown to be harbored not only in urothelium, but also in other cell populations, including blood leukocytes and glial cells [7, 15]. Interestingly, several workers have previously found JCV in lymphoid cell populations, especially, B cells [6]. One results suggest reactivation of JCV in respiratory system [5], thus corresponding to our data on JC positivity in bronchoalveolar cell populations from HSCT patients. Taking into account evident role of donor lymphoid cells in genesis of chronic GvHD [11], one may consider possible role of JC virus as a risk factor for this posttransplant disorder.

In conclusion, the mean incidence rates for the two polyomaviruses depend upon patients’ age and time posttransplant, with different detection rates for distinct clinical samples. Significant correlations between BK, JC, and CMV positivity suggest similar biological mechanisms for their activation in immunosuppressed patients following HSCT.

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Introduction

Hence, the aim of our study was to evaluate time-dependent changes in BKV and JCV positivity in different clinical specimens in the patients undergoing HSCT procedure.

Materials and methods

Patients

Polyomavirus DNA screening

Statistical methods

Results

Types of samples	Age of patients, years
Types of samples	1-10	11-20	21-30	31>
BKV blood	0,33+0,05 (96)	0,30+0,05 (83)	0,33+0,11 (23)	0,37+0,09 (27)
BKV urine	0,61+0,09 (31)	0,79+0,05 (75)	0,87+0,06* (31)	0,89+0,06* (27)
JCV blood	0,20+0,04 (97)	0,16+0,04 (83)	0,15+0,08 (20)	0,23+0,08 (26)
JCV urine	0,28+0,08 (29)	0,44+0,06 (71)	0,32+0,09 (28)	0,48+0,09 (27)